
# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd
import math

# Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# To tune model, get different metric scores and split data
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score
from sklearn.metrics import accuracy_score,precision_score,recall_score
from sklearn import metrics
from sklearn.model_selection import train_test_split

# To impute missing values/data preprocessing
from sklearn.impute import KNNImputer
from sklearn.preprocessing import StandardScaler

# To build a logistic regression model
from sklearn.linear_model import LogisticRegression

# building a Pipelinr
from sklearn.pipeline import Pipeline, make_pipeline

#libraries to help with model building
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import (
    AdaBoostClassifier,
    GradientBoostingClassifier,
    RandomForestClassifier)
from xgboost import XGBClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.preprocessing import OneHotEncoder 
# To suppress the warning
import warnings
warnings.filterwarnings('ignore')


data = pd.read_csv("BankChurners.csv")


data.head(25)


data.shape

(10127, 21)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   CLIENTNUM                 10127 non-null  int64  
 1   Attrition_Flag            10127 non-null  object 
 2   Customer_Age              10127 non-null  int64  
 3   Gender                    10127 non-null  object 
 4   Dependent_count           10127 non-null  int64  
 5   Education_Level           10127 non-null  object 
 6   Marital_Status            10127 non-null  object 
 7   Income_Category           10127 non-null  object 
 8   Card_Category             10127 non-null  object 
 9   Months_on_book            10127 non-null  int64  
 10  Total_Relationship_Count  10127 non-null  int64  
 11  Months_Inactive_12_mon    10127 non-null  int64  
 12  Contacts_Count_12_mon     10127 non-null  int64  
 13  Credit_Limit              10127 non-null  float64
 14  Total_Revolving_Bal       10127 non-null  int64  
 15  Avg_Open_To_Buy           10127 non-null  float64
 16  Total_Amt_Chng_Q4_Q1      10127 non-null  float64
 17  Total_Trans_Amt           10127 non-null  int64  
 18  Total_Trans_Ct            10127 non-null  int64  
 19  Total_Ct_Chng_Q4_Q1       10127 non-null  float64
 20  Avg_Utilization_Ratio     10127 non-null  float64
dtypes: float64(5), int64(10), object(6)
memory usage: 1.6+ MB


data.isna().sum()

CLIENTNUM                   0
Attrition_Flag              0
Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64


data[data.duplicated()].count()

CLIENTNUM                   0
Attrition_Flag              0
Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64


data.nunique()

CLIENTNUM                   10127
Attrition_Flag                  2
Customer_Age                   45
Gender                          2
Dependent_count                 6
Education_Level                 7
Marital_Status                  4
Income_Category                 6
Card_Category                   4
Months_on_book                 44
Total_Relationship_Count        6
Months_Inactive_12_mon          7
Contacts_Count_12_mon           7
Credit_Limit                 6205
Total_Revolving_Bal          1974
Avg_Open_To_Buy              6813
Total_Amt_Chng_Q4_Q1         1158
Total_Trans_Amt              5033
Total_Trans_Ct                126
Total_Ct_Chng_Q4_Q1           830
Avg_Utilization_Ratio         964
dtype: int64


data.describe().T


cat_cols=['Attrition_Flag',     
            'Customer_Age',                
            'Gender',                      
            'Dependent_count',             
            'Education_Level',            
            'Marital_Status',             
            'Income_Category',             
            'Card_Category',               
            'Months_on_book',              
            'Total_Relationship_Count',   
            'Months_Inactive_12_mon',      
            'Contacts_Count_12_mon',       
            'Credit_Limit',                
            'Total_Revolving_Bal',        
            'Avg_Open_To_Buy',             
            'Total_Amt_Chng_Q4_Q1',        
            'Total_Trans_Amt',            
            'Total_Trans_Ct',              
            'Total_Ct_Chng_Q4_Q1',         
            'Avg_Utilization_Ratio']

for column in cat_cols:
    print(data[column].value_counts())
    print('-'*30)

Existing Customer    8500
Attrited Customer    1627
Name: Attrition_Flag, dtype: int64
------------------------------
44    500
49    495
46    490
45    486
47    479
43    473
48    472
50    452
42    426
51    398
53    387
41    379
52    376
40    361
39    333
54    307
38    303
55    279
56    262
37    260
57    223
36    221
35    184
59    157
58    157
34    146
60    127
33    127
32    106
65    101
61     93
62     93
31     91
26     78
30     70
63     65
29     56
64     43
27     32
28     29
67      4
66      2
68      2
73      1
70      1
Name: Customer_Age, dtype: int64
------------------------------
F    5358
M    4769
Name: Gender, dtype: int64
------------------------------
3    2732
2    2655
1    1838
4    1574
0     904
5     424
Name: Dependent_count, dtype: int64
------------------------------
Graduate         3128
High School      2013
Unknown          1519
Uneducated       1487
College          1013
Post-Graduate     516
Doctorate         451
Name: Education_Level, dtype: int64
------------------------------
Married     4687
Single      3943
Unknown      749
Divorced     748
Name: Marital_Status, dtype: int64
------------------------------
Less than $40K    3561
$40K - $60K       1790
$80K - $120K      1535
$60K - $80K       1402
Unknown           1112
$120K +            727
Name: Income_Category, dtype: int64
------------------------------
Blue        9436
Silver       555
Gold         116
Platinum      20
Name: Card_Category, dtype: int64
------------------------------
36    2463
37     358
34     353
38     347
39     341
40     333
31     318
35     317
33     305
30     300
41     297
32     289
28     275
43     273
42     271
29     241
44     230
45     227
27     206
46     197
26     186
47     171
25     165
48     162
24     160
49     141
23     116
22     105
56     103
50      96
21      83
51      80
53      78
20      74
13      70
19      63
52      62
18      58
54      53
55      42
17      39
15      34
16      29
14      16
Name: Months_on_book, dtype: int64
------------------------------
3    2305
4    1912
5    1891
6    1866
2    1243
1     910
Name: Total_Relationship_Count, dtype: int64
------------------------------
3    3846
2    3282
1    2233
4     435
5     178
6     124
0      29
Name: Months_Inactive_12_mon, dtype: int64
------------------------------
3    3380
2    3227
1    1499
4    1392
0     399
5     176
6      54
Name: Contacts_Count_12_mon, dtype: int64
------------------------------
34516.0    508
1438.3     507
15987.0     18
9959.0      18
23981.0     12
          ... 
3891.0       1
19354.0      1
34427.0      1
14527.0      1
8206.0       1
Name: Credit_Limit, Length: 6205, dtype: int64
------------------------------
0       2470
2517     508
1965      12
1480      12
1720      11
        ... 
1401       1
2293       1
2172       1
2040       1
204        1
Name: Total_Revolving_Bal, Length: 1974, dtype: int64
------------------------------
1438.3     324
34516.0     98
31999.0     26
787.0        8
953.0        7
          ... 
2214.0       1
4458.0       1
8013.0       1
33398.0      1
4117.0       1
Name: Avg_Open_To_Buy, Length: 6813, dtype: int64
------------------------------
0.791    36
0.743    34
0.712    34
0.735    33
0.718    33
         ..
0.330     1
1.252     1
2.271     1
0.175     1
1.750     1
Name: Total_Amt_Chng_Q4_Q1, Length: 1158, dtype: int64
------------------------------
4253     11
4509     11
2229     10
4518     10
4869      9
         ..
1810      1
8257      1
14402     1
2174      1
10294     1
Name: Total_Trans_Amt, Length: 5033, dtype: int64
------------------------------
81     208
75     203
71     203
82     202
69     202
      ... 
11       2
138      1
139      1
132      1
134      1
Name: Total_Trans_Ct, Length: 126, dtype: int64
------------------------------
0.667    171
1.000    166
0.500    161
0.750    156
0.600    113
        ... 
1.476      1
0.119      1
1.161      1
1.093      1
1.533      1
Name: Total_Ct_Chng_Q4_Q1, Length: 830, dtype: int64
------------------------------
0.000    2470
0.073      44
0.057      33
0.048      32
0.060      30
         ... 
0.929       1
0.874       1
0.995       1
0.011       1
0.985       1
Name: Avg_Utilization_Ratio, Length: 964, dtype: int64
------------------------------


# While doing uni-variate analysis of numerical variables we want to study their central tendency
# and dispersion.
# Let us write a function that will help us create a boxplot and histogram for any input numerical
# variable.
# This function takes the numerical column as the input and returns the boxplots
# and histograms for the variable.
# Let us see if this helps us write faster and cleaner code.
def histogram_boxplot(feature, figsize=(15, 10), bins=None):
    """Boxplot and histogram combined
    feature: 1-d feature array
    figsize: size of fig (default (9,8))
    bins: number of bins (default None / auto)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.distplot(
        feature, kde=F, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.distplot(
        feature, kde=False, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        np.mean(feature), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        np.median(feature), color="black", linestyle="-"
    )  # Add median to the histogram


histogram_boxplot(data["CLIENTNUM"])


histogram_boxplot(data["Customer_Age"])


histogram_boxplot(data["Dependent_count"])


histogram_boxplot(data["Months_on_book"])


histogram_boxplot(data["Total_Relationship_Count"])


histogram_boxplot(data["Months_Inactive_12_mon"])


histogram_boxplot(data["Contacts_Count_12_mon"])


histogram_boxplot(data["Credit_Limit"])


histogram_boxplot(data["Total_Revolving_Bal"])


histogram_boxplot(data["Avg_Open_To_Buy"])


histogram_boxplot(data["Total_Amt_Chng_Q4_Q1"])


histogram_boxplot(data["Total_Trans_Amt"])


histogram_boxplot(data["Total_Trans_Ct"])


histogram_boxplot(data["Total_Ct_Chng_Q4_Q1"])


histogram_boxplot(data["Avg_Utilization_Ratio"])


def perc_on_bar(feature):
    """
    plot
    feature: categorical feature
    the function won't work if a column is passed in the hue parameter
    """
    # Creating a countplot for the feature
    sns.set(rc={"figure.figsize": (10, 5)})
    ax = sns.countplot(x=feature, data=data)

    total = len(feature)  # length of the column
    for p in ax.patches:
        percentage = "{:.1f}%".format(
            100 * p.get_height() / total
        )  # percentage of each class of the category
        x = p.get_x() + p.get_width() / 2 - 0.1  # width of the plot
        y = p.get_y() + p.get_height()  # hieght of the plot
        ax.annotate(percentage, (x, y), size=14)  # annotate the percantage

    plt.show()  # show the plot


perc_on_bar(data["Attrition_Flag"])


perc_on_bar(data["Marital_Status"])


perc_on_bar(data["Education_Level"])


perc_on_bar(data["Gender"])


perc_on_bar(data["Income_Category"])


perc_on_bar(data["Card_Category"])


sns.pairplot(data, hue="Attrition_Flag")

<seaborn.axisgrid.PairGrid at 0x2cd0f2fdf40>


sns.set(rc={"figure.figsize": (10, 7)})
sns.boxplot(y="Total_Trans_Amt", x="Marital_Status", data=data, orient="vertical")

<AxesSubplot:xlabel='Marital_Status', ylabel='Total_Trans_Amt'>


sns.set(rc={"figure.figsize": (10, 7)})
sns.boxplot(y="Credit_Limit", x="Card_Category", data=data, orient="vertical")

<AxesSubplot:xlabel='Card_Category', ylabel='Credit_Limit'>


sns.set(rc={"figure.figsize": (10, 7)})
sns.boxplot(y="Avg_Utilization_Ratio", x="Attrition_Flag", data=data, orient="vertical")

<AxesSubplot:xlabel='Attrition_Flag', ylabel='Avg_Utilization_Ratio'>


### Function to plot stacked bar charts for categorical columns
def stacked_plot(x):
    sns.set(palette="nipy_spectral")
    tab1 = pd.crosstab(x, data["Attrition_Flag"], margins=True)
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(x, data["Attrition_Flag"], normalize="index")
    tab.plot(kind="bar", stacked=True, figsize=(10, 5))
    plt.legend(loc="lower left", frameon=False)
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


stacked_plot(data["Education_Level"])

Attrition_Flag   Attrited Customer  Existing Customer    All
Education_Level                                             
College                        154                859   1013
Doctorate                       95                356    451
Graduate                       487               2641   3128
High School                    306               1707   2013
Post-Graduate                   92                424    516
Uneducated                     237               1250   1487
Unknown                        256               1263   1519
All                           1627               8500  10127
------------------------------------------------------------------------------------------------------------------------


stacked_plot(data["Gender"])

Attrition_Flag  Attrited Customer  Existing Customer    All
Gender                                                     
F                             930               4428   5358
M                             697               4072   4769
All                          1627               8500  10127
------------------------------------------------------------------------------------------------------------------------


stacked_plot(data["Card_Category"])

Attrition_Flag  Attrited Customer  Existing Customer    All
Card_Category                                              
Blue                         1519               7917   9436
Gold                           21                 95    116
Platinum                        5                 15     20
Silver                         82                473    555
All                          1627               8500  10127
------------------------------------------------------------------------------------------------------------------------


sns.set(rc={"figure.figsize": (15, 15)})
sns.heatmap(
    data.corr(),
    annot=True,
    linewidths=0.5,
    center=0,
    cbar=False,
    cmap="YlGnBu",
    fmt="0.2f",
)
plt.show()


data.drop(columns=["CLIENTNUM","Months_on_book","Total_Trans_Ct","Avg_Open_To_Buy","Total_Revolving_Bal"], inplace=True)


cols = data.select_dtypes(['object'])
cols.columns

Index(['Attrition_Flag', 'Gender', 'Education_Level', 'Marital_Status',
       'Income_Category', 'Card_Category'],
      dtype='object')


for i in cols.columns:
    data[i] = data[i].astype('category')


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 16 columns):
 #   Column                    Non-Null Count  Dtype   
---  ------                    --------------  -----   
 0   Attrition_Flag            10127 non-null  category
 1   Customer_Age              10127 non-null  int64   
 2   Gender                    10127 non-null  category
 3   Dependent_count           10127 non-null  int64   
 4   Education_Level           10127 non-null  category
 5   Marital_Status            10127 non-null  category
 6   Income_Category           10127 non-null  category
 7   Card_Category             10127 non-null  category
 8   Total_Relationship_Count  10127 non-null  int64   
 9   Months_Inactive_12_mon    10127 non-null  int64   
 10  Contacts_Count_12_mon     10127 non-null  int64   
 11  Credit_Limit              10127 non-null  float64 
 12  Total_Amt_Chng_Q4_Q1      10127 non-null  float64 
 13  Total_Trans_Amt           10127 non-null  int64   
 14  Total_Ct_Chng_Q4_Q1       10127 non-null  float64 
 15  Avg_Utilization_Ratio     10127 non-null  float64 
dtypes: category(6), float64(4), int64(6)
memory usage: 851.8 KB


attrition_Flag = {'Existing Customer':1, 'Attrited Customer':0}
data['Attrition_Flag']=data['Attrition_Flag'].map(attrition_Flag)


X = data.drop('Attrition_Flag',axis=1)
y = data['Attrition_Flag']


X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.30, random_state=85, shuffle=True
)
print(X_train.shape, X_test.shape)

(7088, 15) (3039, 15)


col_dummy=['Gender', 'Education_Level', 'Marital_Status',
       'Income_Category', 'Card_Category']


X_train=pd.get_dummies(X_train, columns=col_dummy, drop_first=True)
X_test=pd.get_dummies(X_test, columns=col_dummy, drop_first=True)
print(X_train.shape, X_test.shape)

(7088, 28) (3039, 28)


data.head()


models = []  # Empty list to store all the models

# Appending pipelines for each model into the list
models.append(
    (
        "LR",
        Pipeline(
            steps=[
                ("scaler", StandardScaler()),
                ("log_reg", LogisticRegression(random_state=1)),
            ]
        ),
    )
)
models.append(
    (
        "RF",
        Pipeline(
            steps=[
                ("scaler", StandardScaler()),
                ("random_forest", RandomForestClassifier(random_state=1)),
            ]
        ),
    )
)
models.append(
    (
        "BAG",
        Pipeline(
            steps=[
                ("scaler", StandardScaler()),
                ("BaggingClassifier", BaggingClassifier(random_state=1)),
            ]
        ),
    )
)
models.append(
    (
        "GBM",
        Pipeline(
            steps=[
                ("scaler", StandardScaler()),
                ("gradient_boosting", GradientBoostingClassifier(random_state=1)),
            ]
        ),
    )
)
models.append(
    (
        "ADB",
        Pipeline(
            steps=[
                ("scaler", StandardScaler()),
                ("adaboost", AdaBoostClassifier(random_state=1)),
            ]
        ),
    )
)
models.append(
    (
        "XGB",
        Pipeline(
            steps=[
                ("scaler", StandardScaler()),
                ("xgboost", XGBClassifier(random_state=1,eval_metric='logloss')),
            ]
        ),
    )
)
models.append(
    (
        "DTREE",
        Pipeline(
            steps=[
                ("scaler", StandardScaler()),
                ("decision_tree", DecisionTreeClassifier(random_state=1)),
            ]
        ),
    )
)

results = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models

# loop through all models to get the mean cross validated score
for name, model in models:
    scoring = "recall"
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train, y=y_train, scoring=scoring, cv=kfold, n_jobs=-1
    )
    results.append(cv_result)
    names.append(name)
   
    print("{}: {}".format(name, cv_result.mean() * 100))

LR: 97.46687894147627
RF: 98.8591588516908
BAG: 96.94674471328837
GBM: 98.45657273693863
ADB: 96.87967281175986
XGB: 98.49010165563101
DTREE: 95.25243731611133


# Plotting boxplots for CV scores of all models defined above
fig = plt.figure(figsize=(10, 7))

fig.suptitle("Algorithm Comparison")
ax = fig.add_subplot(111)

plt.boxplot(results)
ax.set_xticklabels(names)

plt.show()


##  Function to calculate different metric scores of the model - Accuracy, Recall and Precision
def get_metrics_score(model, flag=True):
    """
    model: classifier to predict values of X

    """
    # defining an empty list to store train and test results
    score_list = []

    pred_train = model.predict(X_train)
    pred_test = model.predict(X_test)

    train_acc = model.score(X_train, y_train)
    test_acc = model.score(X_test, y_test)

    train_recall = metrics.recall_score(y_train, pred_train)
    test_recall = metrics.recall_score(y_test, pred_test)

    train_precision = metrics.precision_score(y_train, pred_train)
    test_precision = metrics.precision_score(y_test, pred_test)
    
     #F1 score for model
    train_f1 = metrics.f1_score(y_train,pred_train)
    test_f1 = metrics.f1_score(y_test,pred_test)

    score_list.extend(
        (
            train_acc,
            test_acc,
            train_recall,
            test_recall,
            train_precision,
            test_precision,
            train_f1,
            test_f1,
        )
    )

    # If the flag is set to True then only the following print statements will be dispayed. The default value is set to True.
    if flag == True:
        print("Accuracy on training set : ", model.score(X_train, y_train))
        print("Accuracy on test set : ", model.score(X_test, y_test))
        print("Recall on training set : ", metrics.recall_score(y_train, pred_train))
        print("Recall on test set : ", metrics.recall_score(y_test, pred_test))
        print( "Precision on training set : ", metrics.precision_score(y_train, pred_train))
        print("Precision on test set : ", metrics.precision_score(y_test, pred_test))
        print("F1 on training set : ",metrics.f1_score(y_train,pred_train))
        print("F1 on test set : ",metrics.f1_score(y_test,pred_test))

    return score_list  # returning the list with train and test scores


## Function to create confusion matrix
def make_confusion_matrix(model, y_actual, labels=[1, 0]):
    """
    model: classifier to predict values of X
    y_actual: ground truth

    """
    y_predict = model.predict(X_test)
    cm = metrics.confusion_matrix(y_actual, y_predict, labels=[0, 1])
    df_cm = pd.DataFrame(
        cm,
        index=[i for i in ["Actual - No", "Actual - Yes"]],
        columns=[i for i in ["Predicted - No", "Predicted - Yes"]],
    )
    group_counts = ["{0:0.0f}".format(value) for value in cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in cm.flatten() / np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in zip(group_counts, group_percentages)]
    labels = np.asarray(labels).reshape(2, 2)
    plt.figure(figsize=(10, 7))
    sns.heatmap(df_cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


%%time 

# Creating pipeline
pipe = make_pipeline(StandardScaler(), AdaBoostClassifier(random_state=1))

# Parameter grid to pass in GridSearchCV
param_grid = {
    "adaboostclassifier__n_estimators": np.arange(10, 110, 10),
    "adaboostclassifier__learning_rate": [0.1, 0.01, 0.2, 0.05, 1],
    "adaboostclassifier__base_estimator": [
        DecisionTreeClassifier(max_depth=1, random_state=1),
        DecisionTreeClassifier(max_depth=2, random_state=1),
        DecisionTreeClassifier(max_depth=3, random_state=1),
    ],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling GridSearchCV
grid_cv = GridSearchCV(estimator=pipe, param_grid=param_grid, scoring=scorer, cv=5, n_jobs = -1)

# Fitting parameters in GridSeachCV
grid_cv.fit(X_train, y_train)

print(
    "Best Parameters:{} \nScore: {}".format(grid_cv.best_params_, grid_cv.best_score_)
)

Best Parameters:{'adaboostclassifier__base_estimator': DecisionTreeClassifier(max_depth=1, random_state=1), 'adaboostclassifier__learning_rate': 0.1, 'adaboostclassifier__n_estimators': 10} 
Score: 1.0
Wall time: 45.1 s


# Creating new pipeline with best parameters
abc_tuned1 = make_pipeline(
    StandardScaler(),
    AdaBoostClassifier(
        base_estimator=DecisionTreeClassifier(max_depth=2, random_state=1),
        n_estimators=100,
        learning_rate=1,
        random_state=1,
    ),
)

# Fit the model on training data
abc_tuned1.fit(X_train, y_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('adaboostclassifier',
                 AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=2,
                                                                          random_state=1),
                                    learning_rate=1, n_estimators=100,
                                    random_state=1))])


# Calculating different metrics
get_metrics_score(abc_tuned1)

# Creating confusion matrix
make_confusion_matrix(abc_tuned1, y_test)

Accuracy on training set :  0.9841986455981941
Accuracy on test set :  0.9499835472194801
Recall on training set :  0.9929542023150478
Recall on test set :  0.9791256400157542
Precision on training set :  0.988311905159459
Precision on test set :  0.9617021276595744
F1 on training set :  0.9906276150627615
F1 on test set :  0.9703356752537079


feature_names = X_train.columns
importances = abc_tuned1[1].feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


%%time 

# Creating pipeline
pipe = make_pipeline(StandardScaler(), RandomForestClassifier(random_state=1))

# Parameter grid to pass in GridSearchCV
param_grid = {
    "randomforestclassifier__n_estimators": np.arange(10, 110, 10),
    "randomforestclassifier__max_depth": [3, None],
    "randomforestclassifier__min_samples_split": (2, 11),
    "randomforestclassifier__min_samples_leaf": (1, 11),
    "randomforestclassifier__bootstrap": [True, False],
    "randomforestclassifier__criterion": ["gini", "entropy"]
}
        

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling GridSearchCV
grid_cv = GridSearchCV(estimator=pipe, param_grid=param_grid, scoring=scorer, cv=5, n_jobs = -1)

# Fitting parameters in GridSeachCV
grid_cv.fit(X_train, y_train)

print(
    "Best Parameters:{} \nScore: {}".format(grid_cv.best_params_, grid_cv.best_score_)
)

Best Parameters:{'randomforestclassifier__bootstrap': False, 'randomforestclassifier__criterion': 'entropy', 'randomforestclassifier__max_depth': 3, 'randomforestclassifier__min_samples_leaf': 11, 'randomforestclassifier__min_samples_split': 2, 'randomforestclassifier__n_estimators': 20} 
Score: 1.0
Wall time: 1min


# Creating new pipeline with best parameters
RF_tuned1 = make_pipeline(
    StandardScaler(),
    RandomForestClassifier(
        random_state=1,
        n_estimators=100,
        max_depth=4,
        min_samples_split=11,
        min_samples_leaf =1,
        bootstrap=False,
        
    ),
)

# Fit the model on training data
RF_tuned1.fit(X_train, y_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('randomforestclassifier',
                 RandomForestClassifier(bootstrap=False, max_depth=4,
                                        min_samples_split=11,
                                        random_state=1))])


# Calculating different metrics
get_metrics_score(RF_tuned1)

# Creating confusion matrix
make_confusion_matrix(RF_tuned1, y_test)

Accuracy on training set :  0.8807844243792325
Accuracy on test set :  0.8749588680487003
Recall on training set :  0.9994967287367891
Recall on test set :  0.9988184324537219
Precision on training set :  0.8761764705882353
Precision on test set :  0.8705801579128046
F1 on training set :  0.9337826189170128
F1 on test set :  0.9303008070432868


feature_names = X_train.columns
importances = RF_tuned1[1].feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


%%time 

# Creating pipeline
pipe = make_pipeline(
    StandardScaler(), XGBClassifier(random_state=1, eval_metric="logloss")
)

# Parameter grid to pass in GridSearchCV
param_grid = {
    "xgbclassifier__n_estimators": np.arange(50, 300, 50),
    "xgbclassifier__scale_pos_weight": [0, 1, 2, 5, 10],
    "xgbclassifier__learning_rate": [0.01, 0.1, 0.2, 0.05],
    "xgbclassifier__gamma": [0, 1, 3, 5],
    "xgbclassifier__subsample": [0.7, 0.8, 0.9, 1],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling GridSearchCV
grid_cv = GridSearchCV(estimator=pipe, param_grid=param_grid, scoring=scorer, cv=5, n_jobs=-1)

# Fitting parameters in GridSeachCV
grid_cv.fit(X_train, y_train)


print(
    "Best parameters are {} with CV score={}:".format(
        grid_cv.best_params_, grid_cv.best_score_
    )
)

Best parameters are {'xgbclassifier__gamma': 1, 'xgbclassifier__learning_rate': 0.01, 'xgbclassifier__n_estimators': 150, 'xgbclassifier__scale_pos_weight': 10, 'xgbclassifier__subsample': 1} with CV score=0.9984903548102185:
Wall time: 24min 35s


# Creating new pipeline with best parameters
xgb_tuned1 = make_pipeline(
    StandardScaler(),
    XGBClassifier(
        random_state=1,
        n_estimators=50,
        scale_pos_weight=10,
        subsample=0.9,
        learning_rate=0.01,
        gamma=3,
    ),
)

# Fit the model on training data
xgb_tuned1.fit(X_train, y_train)

[20:52:29] WARNING: ..\src\learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('xgbclassifier',
                 XGBClassifier(base_score=0.5, booster='gbtree',
                               colsample_bylevel=1, colsample_bynode=1,
                               colsample_bytree=1, gamma=3, gpu_id=-1,
                               importance_type='gain',
                               interaction_constraints='', learning_rate=0.01,
                               max_delta_step=0, max_depth=6,
                               min_child_weight=1, missing=nan,
                               monotone_constraints='()', n_estimators=50,
                               n_jobs=12, num_parallel_tree=1, random_state=1,
                               reg_alpha=0, reg_lambda=1, scale_pos_weight=10,
                               subsample=0.9, tree_method='exact',
                               validate_parameters=1, verbosity=None))])


# Calculating different metrics
get_metrics_score(xgb_tuned1)

# Creating confusion matrix
make_confusion_matrix(xgb_tuned1, y_test)

Accuracy on training set :  0.8816309255079007
Accuracy on test set :  0.8726554787759131
Recall on training set :  0.999832242912263
Recall on test set :  0.9980307207562032
Precision on training set :  0.8767284495439835
Precision on test set :  0.8689986282578875
F1 on training set :  0.9342424954933772
F1 on test set :  0.9290559120073328


feature_names = X_train.columns
importances = xgb_tuned1[1].feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


%%time

# Creating pipeline
pipe = make_pipeline(StandardScaler(), AdaBoostClassifier(random_state=1))

# Parameter grid to pass in RandomizedSearchCV
param_grid = {
    "adaboostclassifier__n_estimators": np.arange(10, 110, 10),
    "adaboostclassifier__learning_rate": [0.1, 0.01, 0.2, 0.05, 1],
    "adaboostclassifier__base_estimator": [
        DecisionTreeClassifier(max_depth=1, random_state=1),
        DecisionTreeClassifier(max_depth=2, random_state=1),
        DecisionTreeClassifier(max_depth=3, random_state=1),
    ],
}
# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
abc_tuned2 = RandomizedSearchCV(estimator=pipe, param_distributions=param_grid, n_iter=50, scoring=scorer, cv=5,n_jobs=-1, random_state=1)

#Fitting parameters in RandomizedSearchCV
abc_tuned2.fit(X_train,y_train)

print("Best parameters are {} with CV score={}:" .format(abc_tuned2.best_params_,abc_tuned2.best_score_))

Best parameters are {'adaboostclassifier__n_estimators': 50, 'adaboostclassifier__learning_rate': 0.01, 'adaboostclassifier__base_estimator': DecisionTreeClassifier(max_depth=1, random_state=1)} with CV score=1.0:
Wall time: 15.7 s


# Creating new pipeline with best parameters
abc_tuned2 = make_pipeline(
    StandardScaler(),
    AdaBoostClassifier(
        base_estimator=DecisionTreeClassifier(max_depth=2, random_state=1),
        n_estimators=100,
        learning_rate=1,
        random_state=1,
    ),
)

# Fit the model on training data
abc_tuned2.fit(X_train, y_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('adaboostclassifier',
                 AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=2,
                                                                          random_state=1),
                                    learning_rate=1, n_estimators=100,
                                    random_state=1))])


# Calculating different metrics
get_metrics_score(abc_tuned2)

# Creating confusion matrix
make_confusion_matrix(abc_tuned2, y_test)

Accuracy on training set :  0.9841986455981941
Accuracy on test set :  0.9499835472194801
Recall on training set :  0.9929542023150478
Recall on test set :  0.9791256400157542
Precision on training set :  0.988311905159459
Precision on test set :  0.9617021276595744
F1 on training set :  0.9906276150627615
F1 on test set :  0.9703356752537079


feature_names = X_train.columns
importances = abc_tuned2[1].feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


%%time 

# Creating pipeline
pipe = make_pipeline(StandardScaler(), RandomForestClassifier(random_state=1))

# Parameter grid to pass in GridSearchCV
param_grid = {
    "randomforestclassifier__n_estimators": np.arange(10, 110, 10),
    "randomforestclassifier__max_depth": [3, None],
    "randomforestclassifier__min_samples_split": (2, 11),
    "randomforestclassifier__min_samples_leaf": (1, 11),
    "randomforestclassifier__bootstrap": [True, False],
    "randomforestclassifier__criterion": ["gini", "entropy"]
}
        
# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
RF_tuned2 = RandomizedSearchCV(estimator=pipe, param_distributions=param_grid, n_iter=50, scoring=scorer, cv=5,n_jobs=-1, random_state=1)

#Fitting parameters in RandomizedSearchCV
RF_tuned2.fit(X_train,y_train)

print("Best parameters are {} with CV score={}:" .format(RF_tuned2.best_params_,RF_tuned2.best_score_))

Best parameters are {'randomforestclassifier__n_estimators': 80, 'randomforestclassifier__min_samples_split': 2, 'randomforestclassifier__min_samples_leaf': 1, 'randomforestclassifier__max_depth': 3, 'randomforestclassifier__criterion': 'entropy', 'randomforestclassifier__bootstrap': False} with CV score=0.9998323554065381:
Wall time: 9.28 s


# Creating new pipeline with best parameters
RF_tuned2 = make_pipeline(
    StandardScaler(),
    RandomForestClassifier(
        random_state=1,
        n_estimators=100,
        max_depth=4,
        min_samples_split=11,
        min_samples_leaf =1,
        bootstrap=False,
        
    ),
)

# Fit the model on training data
RF_tuned2.fit(X_train, y_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('randomforestclassifier',
                 RandomForestClassifier(bootstrap=False, max_depth=4,
                                        min_samples_split=11,
                                        random_state=1))])


# Calculating different metrics
get_metrics_score(RF_tuned2)

# Creating confusion matrix
make_confusion_matrix(RF_tuned2, y_test)

Accuracy on training set :  0.8807844243792325
Accuracy on test set :  0.8749588680487003
Recall on training set :  0.9994967287367891
Recall on test set :  0.9988184324537219
Precision on training set :  0.8761764705882353
Precision on test set :  0.8705801579128046
F1 on training set :  0.9337826189170128
F1 on test set :  0.9303008070432868


feature_names = X_train.columns
importances = RF_tuned2[1].feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


%%time
#Creating pipeline
pipe=make_pipeline(StandardScaler(),XGBClassifier(random_state=1))

#Parameter grid to pass in RandomizedSearchCV
param_grid={'xgbclassifier__n_estimators':np.arange(50,300,50),'xgbclassifier__scale_pos_weight':[0,1,2,5,10],
            'xgbclassifier__learning_rate':[0.01,0.1,0.2,0.05], 'xgbclassifier__gamma':[0,1,3,5],
            'xgbclassifier__subsample':[0.7,0.8,0.9,1]}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
randomized_cv = RandomizedSearchCV(estimator=pipe, param_distributions=param_grid, n_iter=50, scoring=scorer, cv=5,n_jobs=-1, random_state=1)

#Fitting parameters in RandomizedSearchCV
randomized_cv.fit(X_train,y_train)

print("Best parameters are {} with CV score={}:" .format(randomized_cv.best_params_,randomized_cv.best_score_))

[20:53:40] WARNING: ..\src\learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Best parameters are {'xgbclassifier__subsample': 0.9, 'xgbclassifier__scale_pos_weight': 10, 'xgbclassifier__n_estimators': 200, 'xgbclassifier__learning_rate': 0.01, 'xgbclassifier__gamma': 1} with CV score=0.9976517099185968:
Wall time: 43.9 s


# Creating new pipeline with best parameters
xgb_tuned2 = Pipeline(
    steps=[
        ("scaler", StandardScaler()),
        (
            "XGB",
            XGBClassifier(
                random_state=1,
                n_estimators=20,
                scale_pos_weight=10,
                learning_rate=0.01,
                gamma=1,
                subsample=0.9,
            ),
        ),
    ]
)
# Fit the model on training data
xgb_tuned2.fit(X_train, y_train)

[20:53:41] WARNING: ..\src\learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

Pipeline(steps=[('scaler', StandardScaler()),
                ('XGB',
                 XGBClassifier(base_score=0.5, booster='gbtree',
                               colsample_bylevel=1, colsample_bynode=1,
                               colsample_bytree=1, gamma=1, gpu_id=-1,
                               importance_type='gain',
                               interaction_constraints='', learning_rate=0.01,
                               max_delta_step=0, max_depth=6,
                               min_child_weight=1, missing=nan,
                               monotone_constraints='()', n_estimators=20,
                               n_jobs=12, num_parallel_tree=1, random_state=1,
                               reg_alpha=0, reg_lambda=1, scale_pos_weight=10,
                               subsample=0.9, tree_method='exact',
                               validate_parameters=1, verbosity=None))])


# Calculating different metrics
get_metrics_score(xgb_tuned2)

# Creating confusion matrix
make_confusion_matrix(xgb_tuned2, y_test)

Accuracy on training set :  0.8758465011286681
Accuracy on test set :  0.8706811451135242
Recall on training set :  0.999664485824526
Recall on test set :  0.9980307207562032
Precision on training set :  0.8715811028228755
Precision on test set :  0.867214236824093
F1 on training set :  0.9312392561337708
F1 on test set :  0.9280351583958981


feature_names = X_train.columns
importances = xgb_tuned2[1].feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# defining list of models
models = [abc_tuned1, abc_tuned2, xgb_tuned1, xgb_tuned2,RF_tuned1,RF_tuned2]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_train = []
f1_test = []

# looping through all the models to get the metrics score - Accuracy, Recall and Precision
for model in models:

    j = get_metrics_score(model, False)
    acc_train.append(j[0])
    acc_test.append(j[1])
    recall_train.append(j[2])
    recall_test.append(j[3])
    precision_train.append(j[4])
    precision_test.append(j[5])
    f1_train.append(j[6])
    f1_test.append(j[7])


comparison_frame = pd.DataFrame(
    {
        "Model": [
            "Adaboost with GridSearchCV",
            "Adaboost with RandomizedSearchCV",
            "XGBoost with GridSearchCV",
            "XGBoost with RandomizedSearchCV",
             "RandomForest with GridSearchCV",
            "RandomForest with RandomizedSearchCV",
    
        ],
        "Train_Accuracy": acc_train,
        "Test_Accuracy": acc_test,
        "Train_Recall": recall_train,
        "Test_Recall": recall_test,
        "Train_Precision": precision_train,
        "Test_Precision": precision_test,
        "Train_F1": f1_train,
        "Test_F1": f1_test,
    }
)

# Sorting models in decreasing order of test recall
comparison_frame.sort_values(by="Test_Recall", ascending=False)


feature_names = X_train.columns
importances = RF_tuned1[1].feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

	count	mean	std	min	25%	50%	75%	max
CLIENTNUM	10127.0	7.391776e+08	3.690378e+07	708082083.0	7.130368e+08	7.179264e+08	7.731435e+08	8.283431e+08
Customer_Age	10127.0	4.632596e+01	8.016814e+00	26.0	4.100000e+01	4.600000e+01	5.200000e+01	7.300000e+01
Dependent_count	10127.0	2.346203e+00	1.298908e+00	0.0	1.000000e+00	2.000000e+00	3.000000e+00	5.000000e+00
Months_on_book	10127.0	3.592841e+01	7.986416e+00	13.0	3.100000e+01	3.600000e+01	4.000000e+01	5.600000e+01
Total_Relationship_Count	10127.0	3.812580e+00	1.554408e+00	1.0	3.000000e+00	4.000000e+00	5.000000e+00	6.000000e+00
Months_Inactive_12_mon	10127.0	2.341167e+00	1.010622e+00	0.0	2.000000e+00	2.000000e+00	3.000000e+00	6.000000e+00
Contacts_Count_12_mon	10127.0	2.455317e+00	1.106225e+00	0.0	2.000000e+00	2.000000e+00	3.000000e+00	6.000000e+00
Credit_Limit	10127.0	8.631954e+03	9.088777e+03	1438.3	2.555000e+03	4.549000e+03	1.106750e+04	3.451600e+04
Total_Revolving_Bal	10127.0	1.162814e+03	8.149873e+02	0.0	3.590000e+02	1.276000e+03	1.784000e+03	2.517000e+03
Avg_Open_To_Buy	10127.0	7.469140e+03	9.090685e+03	3.0	1.324500e+03	3.474000e+03	9.859000e+03	3.451600e+04
Total_Amt_Chng_Q4_Q1	10127.0	7.599407e-01	2.192068e-01	0.0	6.310000e-01	7.360000e-01	8.590000e-01	3.397000e+00
Total_Trans_Amt	10127.0	4.404086e+03	3.397129e+03	510.0	2.155500e+03	3.899000e+03	4.741000e+03	1.848400e+04
Total_Trans_Ct	10127.0	6.485869e+01	2.347257e+01	10.0	4.500000e+01	6.700000e+01	8.100000e+01	1.390000e+02
Total_Ct_Chng_Q4_Q1	10127.0	7.122224e-01	2.380861e-01	0.0	5.820000e-01	7.020000e-01	8.180000e-01	3.714000e+00
Avg_Utilization_Ratio	10127.0	2.748936e-01	2.756915e-01	0.0	2.300000e-02	1.760000e-01	5.030000e-01	9.990000e-01

Background & Context¶

Objective¶

Data Dictionary:¶

Index¶

Overview of the dataset ¶

EDA ¶

Bivariate Analysis¶

Split the dataset/Data Pre-processing ¶

Splitting the data into train and test sets¶¶

Model building - Logistic Regression, Bagging, RandomForest, GradientBoosting, AdaBoost, Xgboost, Decision_tree ¶

Hyperparameter tuning using grid search ¶

AdaBoost¶

RandomForest¶

XGBoost¶

Hyperparameter tuning using random search ¶

AdaBoost¶

RandomForest¶

XGBoost¶

Model Performances ¶

Business Recommendations/Actionable Insights ¶

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	...	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	...	1	3	12691.0	777	11914.0	1.335	1144	42	1.625	0.061
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	...	1	2	8256.0	864	7392.0	1.541	1291	33	3.714	0.105
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	...	1	0	3418.0	0	3418.0	2.594	1887	20	2.333	0.000
3	769911858	Existing Customer	40	F	4	High School	Unknown	Less than $40K	Blue	34	...	4	1	3313.0	2517	796.0	1.405	1171	20	2.333	0.760
4	709106358	Existing Customer	40	M	3	Uneducated	Married	$60K - $80K	Blue	21	...	1	0	4716.0	0	4716.0	2.175	816	28	2.500	0.000
5	713061558	Existing Customer	44	M	2	Graduate	Married	$40K - $60K	Blue	36	...	1	2	4010.0	1247	2763.0	1.376	1088	24	0.846	0.311
6	810347208	Existing Customer	51	M	4	Unknown	Married	$120K +	Gold	46	...	1	3	34516.0	2264	32252.0	1.975	1330	31	0.722	0.066
7	818906208	Existing Customer	32	M	0	High School	Unknown	$60K - $80K	Silver	27	...	2	2	29081.0	1396	27685.0	2.204	1538	36	0.714	0.048
8	710930508	Existing Customer	37	M	3	Uneducated	Single	$60K - $80K	Blue	36	...	2	0	22352.0	2517	19835.0	3.355	1350	24	1.182	0.113
9	719661558	Existing Customer	48	M	2	Graduate	Single	$80K - $120K	Blue	36	...	3	3	11656.0	1677	9979.0	1.524	1441	32	0.882	0.144
10	708790833	Existing Customer	42	M	5	Uneducated	Unknown	$120K +	Blue	31	...	3	2	6748.0	1467	5281.0	0.831	1201	42	0.680	0.217
11	710821833	Existing Customer	65	M	1	Unknown	Married	$40K - $60K	Blue	54	...	2	3	9095.0	1587	7508.0	1.433	1314	26	1.364	0.174
12	710599683	Existing Customer	56	M	1	College	Single	$80K - $120K	Blue	36	...	6	0	11751.0	0	11751.0	3.397	1539	17	3.250	0.000
13	816082233	Existing Customer	35	M	3	Graduate	Unknown	$60K - $80K	Blue	30	...	1	3	8547.0	1666	6881.0	1.163	1311	33	2.000	0.195
14	712396908	Existing Customer	57	F	2	Graduate	Married	Less than $40K	Blue	48	...	2	2	2436.0	680	1756.0	1.190	1570	29	0.611	0.279
15	714885258	Existing Customer	44	M	4	Unknown	Unknown	$80K - $120K	Blue	37	...	1	2	4234.0	972	3262.0	1.707	1348	27	1.700	0.230
16	709967358	Existing Customer	48	M	4	Post-Graduate	Single	$80K - $120K	Blue	36	...	2	3	30367.0	2362	28005.0	1.708	1671	27	0.929	0.078
17	753327333	Existing Customer	41	M	3	Unknown	Married	$80K - $120K	Blue	34	...	4	1	13535.0	1291	12244.0	0.653	1028	21	1.625	0.095
18	806160108	Existing Customer	61	M	1	High School	Married	$40K - $60K	Blue	56	...	2	3	3193.0	2517	676.0	1.831	1336	30	1.143	0.788
19	709327383	Existing Customer	45	F	2	Graduate	Married	Unknown	Blue	37	...	1	2	14470.0	1157	13313.0	0.966	1207	21	0.909	0.080
20	806165208	Existing Customer	47	M	1	Doctorate	Divorced	$60K - $80K	Blue	42	...	2	0	20979.0	1800	19179.0	0.906	1178	27	0.929	0.086
21	708508758	Attrited Customer	62	F	0	Graduate	Married	Less than $40K	Blue	49	...	3	3	1438.3	0	1438.3	1.047	692	16	0.600	0.000
22	784725333	Existing Customer	41	M	3	High School	Married	$40K - $60K	Blue	33	...	2	1	4470.0	680	3790.0	1.608	931	18	1.571	0.152
23	811604133	Existing Customer	47	F	4	Unknown	Single	Less than $40K	Blue	36	...	3	2	2492.0	1560	932.0	0.573	1126	23	0.353	0.626
24	789124683	Existing Customer	54	M	2	Unknown	Married	$80K - $120K	Blue	42	...	2	3	12217.0	0	12217.0	1.075	1110	21	0.750	0.000

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision	Train_F1	Test_F1
4	RandomForest with GridSearchCV	0.880784	0.874959	0.999497	0.998818	0.876176	0.870580	0.933783	0.930301
5	RandomForest with RandomizedSearchCV	0.880784	0.874959	0.999497	0.998818	0.876176	0.870580	0.933783	0.930301
2	XGBoost with GridSearchCV	0.881631	0.872655	0.999832	0.998031	0.876728	0.868999	0.934242	0.929056
3	XGBoost with RandomizedSearchCV	0.875847	0.870681	0.999664	0.998031	0.871581	0.867214	0.931239	0.928035
0	Adaboost with GridSearchCV	0.984199	0.949984	0.992954	0.979126	0.988312	0.961702	0.990628	0.970336
1	Adaboost with RandomizedSearchCV	0.984199	0.949984	0.992954	0.979126	0.988312	0.961702	0.990628	0.970336